The aim of this usecase is to conduct a comprehensive analysis of population growth trends across different regions within the City of Melbourne. The objective is to discern potential needs for enhancements in transportation services across various modes available throughout the city. As population densities evolve, ensuring commensurate provisions of stations and stops becomes imperative to facilitate public mobility to and from diverse locales. The overarching goal is to identify areas projected to experience significant population surges and to ascertain corresponding requirements for expanded transportation infrastructure in anticipation of such demographic shifts.
<li> As a urban planner, I want to analyze historical and projected population data across different regions within Melbourne,
So that I can identify areas with significant population growth and prioritize them for transportation infrastructure developmen..
Using "City of Melbourne Population Forecasts by Small Area 2021-2041" dataset we will look at the forecasted population growth based on various features like location, year and value. This will help us understand what has been forecasted population growth in particular locations for particular years and the value of the growth in those areas. Using "City Circle tram stops" dataset we will look into the existing tram stops based on the locations where they exist. This will give us an insight into the number of tram stops in particular location. Using geolocation we can visualize the exact location of these tram stops to aid us into pointing out potential locations for future tram stops. Using "Bus stops" dataset we can see the location of bus stops within Melbourne city, thsi data can aid us to understand the existing number of bus stops in particular locations and if there are need for any more in the future due to the growth in the population. Dataset "Metro Train Stations with accessibility information" gives information about the number of metro stations in various location in Melborune city. This dataset also contains the geolocations of the existing metro stations and using this we can predict if more metro stations will be needed in the future due to the growth in population. The initial approach would be to get the basic information about the dataset and understand the features within each dataset. Later figuring out which features are necessary and discarding the rest that are uncessary and only add to the number of dimensionality of the data. Once the features are understood, the goal will be to clean and remove null values with appropriate tools and techniques. After cleaning, a initial basic visualition to understand the overall distributions of the featues and finding out any correlations amongst them. Finally, using machine learning models to predict the number of transportation services that might be needed to aid the population growth within the city in particular locations. Potenitally, using PowerBI to the convey insights to the stakeholders/business owners/policy makers to help them understand the rise in population and their need for public transportation services, thus helping them plan for such resources beforehand.
17052
| geography | year | gender | age | value | |
|---|---|---|---|---|---|
| 0 | City of Melbourne | 2022 | Female | Age 0-4 | 2212.0 |
| 1 | City of Melbourne | 2024 | Female | Age 0-4 | 2818.0 |
| 2 | City of Melbourne | 2029 | Female | Age 0-4 | 4310.0 |
| 3 | City of Melbourne | 2031 | Female | Age 0-4 | 4736.0 |
| 4 | City of Melbourne | 2032 | Female | Age 0-4 | 4931.0 |
28
| geo_point_2d | geo_shape | name | xorg | stop_no | mccid_str | xsource | xdate | mccid_int | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | -37.81922319307822, 144.9614014008424 | {"coordinates": [144.9614014008424, -37.819223... | Market Street / Flinders Street | GIS Team | 3 | NaN | Mapbase | 2011-10-18 | 3 |
| 1 | -37.821539117626855, 144.95356912978238 | {"coordinates": [144.95356912978238, -37.82153... | Victoria Police Centre / Flinders Street | GIS Team | D6 | NaN | Mapbase | 2011-10-18 | 6 |
| 2 | -37.815426586135686, 144.94512063442602 | {"coordinates": [144.94512063442602, -37.81542... | Central Pier / Harbour Esplanade | GIS Team | D2 | NaN | Mapbase | 2011-10-18 | 10 |
309
| geo_point_2d | geo_shape | prop_id | addresspt1 | addressp_1 | asset_clas | asset_type | objectid | str_id | addresspt | asset_subt | model_desc | mcc_id | roadseg_id | descriptio | model_no | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -37.80384165792465, 144.93239283833262 | {"coordinates": [144.93239283833262, -37.80384... | 0 | 76.819824 | 357 | Signage | Sign - Public Transport | 355 | 1235255 | 570648 | NaN | Sign - Public Transport 1 Panel | 1235255 | 21673 | Sign - Public Transport 1 Panel Bus Stop Type 13 | P.16 |
| 1 | -37.81548699581418, 144.9581794249902 | {"coordinates": [144.9581794249902, -37.815486... | 0 | 21.561304 | 83 | Signage | Sign - Public Transport | 600 | 1231226 | 548056 | NaN | Sign - Public Transport 1 Panel | 1231226 | 20184 | Sign - Public Transport 1 Panel Bus Stop Type 8 | P.16 |
| 2 | -37.81353897396532, 144.95728334230756 | {"coordinates": [144.95728334230756, -37.81353... | 0 | 42.177187 | 207 | Signage | Sign - Public Transport | 640 | 1237092 | 543382 | NaN | Sign - Public Transport 1 Panel | 1237092 | 20186 | Sign - Public Transport 1 Panel Bus Stop Type 8 | P.16 |
219
| geo_point_2d | geo_shape | he_loop | lift | pids | station | |
|---|---|---|---|---|---|---|
| 0 | -37.77839599999999, 145.031251 | {"coordinates": [145.031251, -37.7783959999999... | No | No | Dot Matrix | Alphington |
| 1 | -37.86724899999996, 144.830604 | {"coordinates": [144.830604, -37.8672489999999... | No | No | LCD | Altona |
| 2 | -37.761897999999974, 144.96056099999998 | {"coordinates": [144.96056099999998, -37.76189... | No | No | No | Anstey |
The following code blocks will briefly visualize the information in our dataset. This is just a initial visualization to understand the overall pattern in our data.
Population dataset
pop.head(5)| geography | year | value | |
|---|---|---|---|
| 5620 | City of Melbourne | 2021 | 0.498634 |
| 5621 | City of Melbourne | 2025 | 0.580116 |
| 5622 | City of Melbourne | 2028 | 0.691288 |
| 5623 | City of Melbourne | 2029 | 0.723781 |
| 5624 | City of Melbourne | 2033 | 0.836179 |
The code below takes the latitude and longitude from the datasets and finds their location on map. If a particular location is not found the code outputs a "CITY NOT FOUND" message. Finally a new dataframe is created to store the information for latitude and longitude and corresponding city name.
CITY NOT FOUND: Melbourne (Remainder), Victoria, Australia
CITY NOT FOUND: West Melbourne (Residential), Victoria, Australia
| geography | city_lat | city_long | |
|---|---|---|---|
| 0 | City of Melbourne | -37.812382 | 144.948265 |
| 1 | Carlton | -37.800423 | 144.968434 |
| 2 | Docklands | -37.817542 | 144.939492 |
| 3 | East Melbourne | -37.812498 | 144.985885 |
| 4 | Kensington | -37.793938 | 144.930565 |
| 5 | Melbourne | -37.814171 | 144.965562 |
| 6 | North Melbourne | -37.807609 | 144.942351 |
| 7 | Parkville | -37.787115 | 144.951553 |
| 8 | Port Melbourne | -37.833361 | 144.921920 |
| 9 | South Yarra | -37.837770 | 144.991854 |
| 10 | Southbank | -37.825362 | 144.964020 |
| 11 | Australia Post Sunshine West PDC | -37.808367 | 144.813062 |
sns.move_legend(plot, "upper left", bbox_to_anchor=(1, 1))
Key findings from the plot:
This scatterplot visualizes population growth trends over time for various locations within Melbourne. Here's what can be understood from the plot:
Dominant Growth in the City of Melbourne: The red line, representing the City of Melbourne, shows a strong upward trend, indicating a significant increase in population from 2020 to 2040. This area is experiencing more substantial growth compared to other locations.
Stable Populations in Other Areas: The green line, representing Carlton, and other colored lines for various districts such as Docklands, East Melbourne, and others, show relatively stable populations over the same period. These lines are mostly flat, indicating little to no growth in population.
Cluster of Locations with Minimal Change: Most of the other locations represented by various colors remain nearly constant over the years. This suggests that while the central area of Melbourne is growing, other districts are not experiencing similar population increases.
Potential Focus for Infrastructure Development: Given the significant growth in the City of Melbourne, there might be an increased need for infrastructure and transportation services in this area to accommodate the rising population.
Comparison of Growth Rates: The graph allows for an easy comparison of growth rates between the central business district and the outlying areas, highlighting areas that might require more attention from urban planners and policymakers.
Overall, this plot is crucial for planning and resource allocation, especially in targeting areas with rapid population growth for transportation and infrastructure development.icdpment.

This bar chart visualizes the overall population distribution across various geographic areas within Melbourne. Here’s what can be understood from the plot:
Dominant Population in the City of Melbourne: The bar for the City of Melbourne is significantly longer than for any other area, indicating that it has a much higher population compared to other parts of the city.
Comparatively Lower Populations in Other Areas: Areas like Carlton, Docklands, and Kensington have relatively smaller populations, as shown by the shorter bars.
Minimal Population in Some Suburbs: Areas like Southbank, West Melbourne (both Industrial and Residential), and Port Melbourne have even smaller populations relative to the central and other urban areas, indicated by the shortest bars.
Potential Areas for Infrastructure Focus: Given the high population in the City of Melbourne, this area might require more robust transportation and infrastructure services. Conversely, areas with smaller populations might be evaluated for potential growth or revitalization projects.
Strategic Planning for Service Allocation: Urban planners and decision-makers can use this data to strategically plan resource allocation, ensuring that areas with higher populations are adequately served, potentially planning for expansion in areas showing a potential for growth.
Overall, this plot serves as a crucial tool for understanding how population is spread across Melbourne, guiding decisions related to urban planning, transportation services, and infrastructure development.

This box plot visualizes the distribution of population values over years for various locations within Melbourne. Here's what the graph tells us:
Overall, this box plot is particularly useful for understanding where population pressures are greatest and where they are most stable, assisting city planners and policymakers in making informed decisions about where to allocate resources and how to plan for future development or conservation.
Tram dataset
tram.head(3)| stop_name | lat_tram | lon_tram | |
|---|---|---|---|
| 0 | Market Street / Flinders Street | -37.819223 | 144.961401 |
| 1 | Victoria Police Centre / Flinders Street | -37.821539 | 144.953569 |
| 2 | Central Pier / Harbour Esplanade | -37.815427 | 144.945121 |
mapAs the tram data is limited to the central square we will no longer use this dataset in our usecase
Metro station dataset
mapBus stop dataset
As the data for Tram stops is not sufficient we will no longer use tram data
Now the data is staionary as the p-value is below 0.05

2042-01-01 0.001606
2043-01-01 0.001054
2044-01-01 0.001233
2045-01-01 0.001175
2046-01-01 0.001194
Freq: AS-JAN, Name: predicted_mean, dtype: float64

Key findings from the plot.

Code that goes through all cities

Key findings from the plot.
General approach explained/shown below


Key findings from the plot.
The plot above shows the clusters of bus stops with different colours and later on real world map. This is helpful in understanding how the bus stops are distributed within the city and potenatial areas of interest for future development.
Accessibility Analysis: Understanding how far stops are from one another can help assess the accessibility of public transportation for residents. Areas poorly served can be identified and addressed.
Service Frequency Adjustments: Distance data can be used to help determine where to increase service frequencies or capacities, particularly in rapidly growing or heavily trafficked areas.
underserved_distance_df| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.000000 | 1.964545 | 2.605692 | 3.652704 | 6.895595 | 2.115379 | 1.873523 | 1.980960 | 3.647904 | 2.177803 | ... | 3.447816 | 3.699037 | 2.404622 | 1.953986 | 3.232718 | 3.349753 | 1.988170 | 3.642521 | 3.242434 | 2.121221 |
| 1 | 1.964545 | 0.000000 | 0.862403 | 5.357905 | 5.725009 | 0.523640 | 3.787898 | 0.032375 | 2.488723 | 0.619123 | ... | 5.122267 | 2.518697 | 0.805197 | 0.288873 | 2.119326 | 5.228514 | 0.400413 | 5.351613 | 2.119995 | 0.776542 |
| 2 | 2.605692 | 0.862403 | 0.000000 | 6.146014 | 6.084751 | 1.265593 | 4.476681 | 0.830551 | 1.709769 | 1.335583 | ... | 5.917246 | 1.730613 | 0.244921 | 0.695853 | 1.392195 | 5.945374 | 0.629202 | 6.138600 | 1.388683 | 0.559538 |
| 3 | 3.652704 | 5.357905 | 6.146014 | 0.000000 | 8.173400 | 5.251295 | 1.961359 | 5.384167 | 7.297522 | 5.262132 | ... | 0.258265 | 7.349403 | 5.978949 | 5.452847 | 6.883566 | 0.981970 | 5.517946 | 0.019532 | 6.893570 | 5.726557 |
| 4 | 6.895595 | 5.725009 | 6.084751 | 8.173400 | 0.000000 | 5.234256 | 7.869994 | 5.741710 | 7.606925 | 5.136704 | ... | 7.932268 | 7.603056 | 6.245052 | 5.992522 | 7.404850 | 8.725853 | 6.085721 | 8.181785 | 7.397035 | 6.410958 |
| 5 | 2.115379 | 0.523640 | 1.265593 | 5.251295 | 5.234256 | 0.000000 | 3.828361 | 0.548972 | 2.957687 | 0.098221 | ... | 5.006380 | 2.983183 | 1.275673 | 0.812217 | 2.605959 | 5.218645 | 0.922308 | 5.246818 | 2.605329 | 1.295189 |
| 6 | 1.873523 | 3.787898 | 4.476681 | 1.961359 | 7.869994 | 3.828361 | 0.000000 | 3.808405 | 5.434710 | 3.867979 | ... | 1.819885 | 5.488969 | 4.278065 | 3.812646 | 5.028177 | 1.481333 | 3.854608 | 1.946689 | 5.039425 | 3.993454 |
| 7 | 1.980960 | 0.032375 | 0.830551 | 5.384167 | 5.741710 | 0.548972 | 3.808405 | 0.000000 | 2.456910 | 0.643576 | ... | 5.148906 | 2.486769 | 0.773025 | 0.263506 | 2.088139 | 5.250875 | 0.373512 | 5.377809 | 2.088744 | 0.748495 |
| 8 | 3.647904 | 2.488723 | 1.709769 | 7.297522 | 7.606925 | 2.957687 | 5.434710 | 2.456910 | 0.000000 | 3.034830 | ... | 7.095550 | 0.058454 | 1.686901 | 2.232070 | 0.417102 | 6.905076 | 2.122394 | 7.286840 | 0.408922 | 1.771010 |
| 9 | 2.177803 | 0.619123 | 1.335583 | 5.262132 | 5.136704 | 0.098221 | 3.867979 | 0.643576 | 3.034830 | 0.000000 | ... | 5.015702 | 3.059245 | 1.357261 | 0.907073 | 2.687763 | 5.247484 | 1.016249 | 5.258009 | 2.686810 | 1.387397 |
| 10 | 3.349633 | 5.225195 | 5.944031 | 0.959205 | 8.708764 | 5.213046 | 1.482751 | 5.247659 | 6.908837 | 5.241482 | ... | 1.037900 | 6.963634 | 5.750991 | 5.270362 | 6.504754 | 0.023172 | 5.318233 | 0.939775 | 6.516252 | 5.470838 |
| 11 | 2.605422 | 1.490369 | 0.902812 | 6.257095 | 6.986316 | 1.998015 | 4.436285 | 1.460649 | 1.082806 | 2.086017 | ... | 6.048354 | 1.126662 | 0.756358 | 1.211003 | 0.672313 | 5.916120 | 1.097252 | 6.247153 | 0.677682 | 0.723378 |
| 12 | 1.888444 | 3.766008 | 4.242536 | 3.200500 | 8.673949 | 3.987805 | 1.377420 | 3.776194 | 4.776766 | 4.055777 | ... | 3.110217 | 4.834613 | 4.008911 | 3.685674 | 4.405896 | 2.441530 | 3.686926 | 3.183162 | 4.419577 | 3.693773 |
| 13 | 3.419497 | 5.095025 | 5.889580 | 0.280367 | 7.918997 | 4.980065 | 1.793195 | 5.121636 | 7.067226 | 4.989596 | ... | 0.028334 | 7.118497 | 5.726087 | 5.195340 | 6.652210 | 1.053551 | 5.262397 | 0.281360 | 6.661930 | 5.477971 |
| 14 | 6.398067 | 5.226534 | 5.599598 | 7.761954 | 0.502333 | 4.733933 | 7.400295 | 5.243554 | 7.140102 | 4.636282 | ... | 7.517611 | 7.137717 | 5.756028 | 5.495329 | 6.929357 | 8.287751 | 5.589363 | 7.769696 | 6.921852 | 5.917284 |
| 15 | 3.058493 | 4.882643 | 5.626934 | 0.787663 | 8.261705 | 4.842547 | 1.246706 | 4.906396 | 6.668694 | 4.866344 | ... | 0.753944 | 6.722282 | 5.442663 | 4.944404 | 6.259371 | 0.464208 | 4.998415 | 0.769543 | 6.270229 | 5.171987 |
| 16 | 3.382004 | 2.019774 | 1.201389 | 7.029773 | 7.075162 | 2.463999 | 5.220307 | 1.987475 | 0.537661 | 2.536320 | ... | 6.817676 | 0.545043 | 1.215758 | 1.782836 | 0.428401 | 6.700487 | 1.679198 | 7.020179 | 0.413865 | 1.360286 |
| 17 | 5.721497 | 5.161008 | 5.788275 | 6.206659 | 2.211076 | 4.638278 | 6.255699 | 5.187101 | 7.480752 | 4.545146 | ... | 5.981621 | 7.493931 | 5.871543 | 5.449881 | 7.180367 | 6.873996 | 5.560558 | 6.217458 | 7.176955 | 5.932460 |
| 18 | 1.988442 | 0.499152 | 0.617281 | 5.544727 | 6.182679 | 1.021811 | 3.859541 | 0.472855 | 2.042671 | 1.116009 | ... | 5.319092 | 2.077542 | 0.435431 | 0.210613 | 1.655828 | 5.329223 | 0.100071 | 5.536886 | 1.658054 | 0.283129 |
| 19 | 2.230139 | 1.001594 | 0.629495 | 5.861624 | 6.606517 | 1.518718 | 4.093437 | 0.973250 | 1.565506 | 1.610146 | ... | 5.645387 | 1.604885 | 0.396872 | 0.716591 | 1.165310 | 5.574140 | 0.602848 | 5.852554 | 1.169031 | 0.225495 |
| 20 | 2.727331 | 4.470104 | 4.823519 | 3.848951 | 9.588992 | 4.757246 | 2.236593 | 4.475347 | 5.058680 | 4.833652 | ... | 3.807178 | 5.117086 | 4.580733 | 4.345391 | 4.730818 | 2.961302 | 4.326824 | 3.830170 | 4.745720 | 4.264061 |
| 21 | 2.297553 | 0.825901 | 0.396501 | 5.893864 | 6.361458 | 1.324536 | 4.170486 | 0.794984 | 1.670286 | 1.411497 | ... | 5.671672 | 1.703151 | 0.152409 | 0.561796 | 1.293872 | 5.647148 | 0.453112 | 5.885542 | 1.294834 | 0.180337 |
| 22 | 2.273955 | 1.039889 | 0.625999 | 5.907232 | 6.627715 | 1.554886 | 4.135878 | 1.011105 | 1.518182 | 1.645575 | ... | 5.691338 | 1.557378 | 0.401759 | 0.756530 | 1.118826 | 5.616764 | 0.642651 | 5.898119 | 1.122398 | 0.266321 |
| 23 | 3.373368 | 2.019579 | 1.204055 | 7.021662 | 7.085735 | 2.465801 | 5.210515 | 1.987298 | 0.529282 | 2.538548 | ... | 6.809866 | 0.538203 | 1.215155 | 1.781241 | 0.412393 | 6.690564 | 1.677168 | 7.012034 | 0.397893 | 1.356150 |
| 24 | 3.061395 | 4.888211 | 5.631341 | 0.796153 | 8.275103 | 4.849555 | 1.246051 | 4.911903 | 6.669877 | 4.873607 | ... | 0.765930 | 6.723517 | 5.446656 | 4.949175 | 6.260739 | 0.450751 | 5.002902 | 0.777922 | 6.271624 | 5.175533 |
| 25 | 3.001721 | 4.727999 | 5.061577 | 4.000947 | 9.864996 | 5.023083 | 2.464372 | 4.732550 | 5.236883 | 5.100393 | ... | 3.973937 | 5.295146 | 4.817953 | 4.597239 | 4.919959 | 3.086350 | 4.575751 | 3.981883 | 4.935047 | 4.502087 |
| 26 | 3.971509 | 2.730833 | 1.914575 | 7.623043 | 7.668399 | 3.178099 | 5.766470 | 2.698610 | 0.335323 | 3.249989 | ... | 7.419246 | 0.278772 | 1.925743 | 2.487252 | 0.739931 | 7.238062 | 2.380927 | 7.612537 | 0.729594 | 2.044785 |
| 27 | 4.661525 | 4.548637 | 5.305764 | 4.770735 | 3.533046 | 4.055865 | 4.930908 | 4.578880 | 7.011088 | 3.976567 | ... | 4.547485 | 7.034669 | 5.331463 | 4.827759 | 6.661241 | 5.455998 | 4.941635 | 4.781711 | 6.660841 | 5.318092 |
| 28 | 4.110928 | 2.293540 | 1.508387 | 7.641870 | 6.214769 | 2.527046 | 5.983501 | 2.265456 | 1.655829 | 2.557760 | ... | 7.409529 | 1.624519 | 1.708278 | 2.192023 | 1.699551 | 7.453621 | 2.135908 | 7.634966 | 1.684790 | 2.007120 |
| 29 | 2.392312 | 1.227472 | 0.732337 | 6.037266 | 6.784782 | 1.740498 | 4.241953 | 1.198411 | 1.343230 | 1.830366 | ... | 5.824694 | 1.384565 | 0.540194 | 0.944975 | 0.938186 | 5.723246 | 0.831089 | 6.027759 | 0.942580 | 0.454964 |
| 30 | 2.491369 | 4.248072 | 4.619014 | 3.735797 | 9.352320 | 4.528191 | 2.054982 | 4.253926 | 4.907636 | 4.603812 | ... | 3.680274 | 4.966090 | 4.377157 | 4.128810 | 4.570437 | 2.877173 | 4.112878 | 3.717340 | 4.585149 | 4.060017 |
| 31 | 2.042856 | 3.848772 | 4.268870 | 3.490453 | 8.890213 | 4.105762 | 1.690192 | 3.856490 | 4.689088 | 4.178584 | ... | 3.408118 | 4.747350 | 4.030363 | 3.746226 | 4.331734 | 2.701038 | 3.738032 | 3.472806 | 4.345910 | 3.713213 |
| 32 | 2.200662 | 0.828237 | 0.508592 | 5.808248 | 6.426399 | 1.341205 | 4.072343 | 0.798967 | 1.697951 | 1.431598 | ... | 5.587817 | 1.733870 | 0.263684 | 0.548287 | 1.309058 | 5.550330 | 0.434730 | 5.799701 | 1.311367 | 0.081993 |
| 33 | 3.092739 | 1.739633 | 0.949892 | 6.737597 | 6.930871 | 2.198855 | 4.938042 | 1.707502 | 0.760664 | 2.274959 | ... | 6.524328 | 0.784365 | 0.934541 | 1.494681 | 0.473998 | 6.418989 | 1.389028 | 6.728146 | 0.466248 | 1.063513 |
| 34 | 4.062902 | 2.811675 | 1.989924 | 7.714464 | 7.707192 | 3.254656 | 5.857569 | 2.779407 | 0.424903 | 3.325493 | ... | 7.510628 | 0.369103 | 2.006955 | 2.570486 | 0.831336 | 7.328927 | 2.464785 | 7.703960 | 0.821018 | 2.131207 |
| 35 | 2.412964 | 0.824310 | 0.249837 | 5.990674 | 6.262406 | 1.295602 | 4.286475 | 0.792166 | 1.667277 | 1.377232 | ... | 5.766003 | 1.695807 | 0.019980 | 0.590839 | 1.310595 | 5.760684 | 0.495394 | 5.982682 | 1.309801 | 0.317819 |
| 36 | 2.005453 | 0.192016 | 0.997265 | 5.313177 | 5.544317 | 0.331624 | 3.794512 | 0.217929 | 2.658271 | 0.427329 | ... | 5.073914 | 2.686445 | 0.971371 | 0.480702 | 2.295840 | 5.218909 | 0.591421 | 5.307536 | 2.295969 | 0.966086 |
| 37 | 3.447816 | 5.122267 | 5.917246 | 0.258265 | 7.932268 | 5.006380 | 1.819885 | 5.148906 | 7.095550 | 5.015702 | ... | 0.000000 | 7.146819 | 5.753931 | 5.222953 | 6.680530 | 1.061034 | 5.290118 | 0.260593 | 6.690248 | 5.505982 |
| 38 | 3.699037 | 2.518697 | 1.730613 | 7.349403 | 7.603056 | 2.983183 | 5.488969 | 2.486769 | 0.058454 | 3.059245 | ... | 7.146819 | 0.000000 | 1.715309 | 2.264854 | 0.466515 | 6.959940 | 2.155878 | 7.338780 | 0.457382 | 1.807876 |
| 39 | 2.404622 | 0.805197 | 0.244921 | 5.978949 | 6.245052 | 1.275673 | 4.278065 | 0.773025 | 1.686901 | 1.357261 | ... | 5.753931 | 1.715309 | 0.000000 | 0.573756 | 1.330556 | 5.751738 | 0.479656 | 5.971005 | 1.329751 | 0.317377 |
| 40 | 1.953986 | 0.288873 | 0.695853 | 5.452847 | 5.992522 | 0.812217 | 3.812646 | 0.263506 | 2.232070 | 0.907073 | ... | 5.222953 | 2.264854 | 0.573756 | 0.000000 | 1.852364 | 5.272430 | 0.113885 | 5.445628 | 1.853911 | 0.491097 |
| 41 | 3.232718 | 2.119326 | 1.392195 | 6.883566 | 7.404850 | 2.605959 | 5.028177 | 2.088139 | 0.417102 | 2.687763 | ... | 6.680530 | 0.466515 | 1.330556 | 1.852364 | 0.000000 | 6.501348 | 1.740457 | 6.873003 | 0.015654 | 1.378238 |
| 42 | 3.349753 | 5.228514 | 5.945374 | 0.981970 | 8.725853 | 5.218645 | 1.481333 | 5.250875 | 6.905076 | 5.247484 | ... | 1.061034 | 6.959940 | 5.751738 | 5.272430 | 6.501348 | 0.000000 | 5.319849 | 0.962549 | 6.512884 | 5.470972 |
| 43 | 1.988170 | 0.400413 | 0.629202 | 5.517946 | 6.085721 | 0.922308 | 3.854608 | 0.373512 | 2.122394 | 1.016249 | ... | 5.290118 | 2.155878 | 0.479656 | 0.113885 | 1.740457 | 5.319849 | 0.000000 | 5.510418 | 1.742191 | 0.377366 |
| 44 | 3.642521 | 5.351613 | 6.138600 | 0.019532 | 8.181785 | 5.246818 | 1.946689 | 5.377809 | 7.286840 | 5.258009 | ... | 0.260593 | 7.338780 | 5.971005 | 5.445628 | 6.873003 | 0.962549 | 5.510418 | 0.000000 | 6.883035 | 5.718032 |
| 45 | 3.242434 | 2.119995 | 1.388683 | 6.893570 | 7.397035 | 2.605329 | 5.039425 | 2.088744 | 0.408922 | 2.686810 | ... | 6.690248 | 0.457382 | 1.329751 | 1.853911 | 0.015654 | 6.512884 | 1.742191 | 6.883035 | 0.000000 | 1.381048 |
| 46 | 2.121221 | 0.776542 | 0.559538 | 5.726557 | 6.410958 | 1.295189 | 3.993454 | 0.748495 | 1.771010 | 1.387397 | ... | 5.505982 | 1.807876 | 0.317377 | 0.491097 | 1.378238 | 5.470972 | 0.377366 | 5.718032 | 1.381048 | 0.000000 |
47 rows × 47 columns

This heatmap displays a distance matrix for underserved bus stops, where each cell represents the distance between pairs of bus stops, indexed from 0 to about 46. The colors indicate the magnitude of the distance, with darker colors (closer to blue and purple) indicating shorter distances and lighter colors (towards yellow) indicating greater distances. Here's what the heatmap tells us:
Close Proximity Patterns: The clusters of darker colors (blues and purples) suggest that certain bus stops are located very close to each other. This could be indicative of densely populated areas or regions where stops are closely spaced.
Distance Variation: There are sporadic patches of lighter colors (yellows and greens), indicating longer distances between certain bus stops. This variation can signal areas where service might be less frequent or accessible, potentially indicating regions that are underserved and might benefit from additional stops or enhanced services.
Diagonal Line of Darkness: The diagonal from the top left to the bottom right, which is uniformly dark, represents the distance from each bus stop to itself, which is naturally zero. This is a standard feature in distance matrices.
Identifying Potential Service Gaps: The areas with lighter squares between darker clusters could be key locations for adding new bus stops or improving transit connectivity. These gaps in service might represent physical barriers (like rivers or highways) or simply areas that have been overlooked in transit planning.
Strategic Planning: Planners can use this information to optimize routes, considering where to place new stops or how to reroute existing lines to reduce overall travel times and improve service coverage in underserved areas.
Overall, this heatmap provides a visual tool for identifying how evenly distributed and accessible bus transportation services are across a region, highlighting both well-served areas and potential gaps in service.
Based on the map visualization showing bus stop distribution with red markers for underserved stops and purple markers for other stops, and considering the violet lines representing distances greater than 7 kilometers between bus stops, we can deduce several important pieces of information about current bus stop distribution and potential locations for new stops:
High Concentration Areas: The central area, particularly around the Melbourne CBD, has a high concentration of bus stops, both underserved (red) and otherwise (purple). This suggests a well-serviced urban core which might not require additional stops but could benefit from other types of service improvements such as increased frequency or extended hours.
Long Distance Between Stops: The violet lines, indicating distances greater than 7 kilometers between certain stops, show significant gaps in service coverage. These lines, especially those stretching out to areas with fewer or no stops, highlight regions where new stops could dramatically improve service.
Identifying Underserved Regions: The red markers signify underserved bus stops, potentially due to their isolation from other stops or because they serve areas with insufficient coverage. Placing new stops between these red-marked underserved stops and the nearest purple stops could enhance connectivity.
Spatial Distribution of Service Gaps: The distribution of violet lines shows not just linear distances but also the geographic spread of areas lacking adequate service. The southern and eastern peripheries of the map, for instance, show longer distances between stops, suggesting that these are likely areas where public transit accessibility could be improved.
Strategic Placement for New Stops: The areas without any markers or with sparse coverage by purple markers are potential zones for developing new bus stops. Strategic placement in these zones can help bridge the long gaps shown by the violet lines, ensuring a more evenly distributed transit network that can cater to a larger population and reduce transit deserts.
Optimization Opportunities: Analyzing the overlap of long-distance lines and existing bus stops can offer insights into optimizing current routes. Reconfiguring some routes might address several gaps without necessarily adding many new stops, especially if existing stops are underutilized or inefficiently positioned.
This map is a valuable tool for transit planners aiming to enhance bus service coverage, efficiency, and accessibility in Melbourne. It illustrates not only where services are currently lacking but also how existing resources might be better utilized or expanded to meet the city’s transportation needs more effectively.